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Climate aware task scheduler

Project description

CATS: Climate-Aware Task Scheduler

CATS is a Climate-Aware Task Scheduler. It schedules cluster jobs to minimize predicted carbon intensity of running the process. It was created as part of the 2023 Collaborations Workshop.

CATS

The Climate-Aware Task Scheduler is a lightweight Python package designed to schedule tasks based on the estimated carbon intensity of the electricity grid at any given moment. This tool uses real-time carbon intensity data from the National Grid ESO via their API to estimate the carbon intensity of the electricity grid, and schedules tasks at times when the estimated carbon intensity is lowest. This helps to reduce the carbon emissions associated with running computationally intensive tasks, making it an ideal solution for environmentally conscious developers.

Currently CATS only works in the UK. If you are aware of APIs for realtime grid carbon intensity data in other countries please open an issue and let us know.

Features

  • Estimates the carbon intensity of the electricity grid in real-time
  • Schedules tasks based on the estimated carbon intensity, minimizing carbon emissions
  • Provides a simple and intuitive API for developers
  • Lightweight and easy to integrate into existing workflows
  • Supports Python 3.9+

Installation

Install via pip as follows:

pip install climate-aware-task-scheduler

To install the development version:

pip install git+https://github.com/GreenScheduler/cats

Documentation

Documentation is available at greenscheduler.github.io/cats/.

We recommend the quickstart if you are new to CATS. CATS can optionally display carbon footprint savings using a configuration file.

Console demonstration

CATS predicting optimal start time for the ls command in the OX1 postcode:

CATS animated usage example

Contributing

We welcome contributions from the community! If you find a bug or have an idea for a new feature, please open an issue on our GitHub repository or submit a pull request.

License

MIT License

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